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@k16shikano
k16shikano / SKILL.md
Last active June 22, 2026 03:38
japanese-tech-writing/SKILL
name japanese-tech-writing
description 日本語の技術文書・書籍原稿の文章規範。整形(一文一行、引用ブロック、脚注、コラム記法)、段落と論証の構成(パラグラフライティング)、論証の厳密さ(ツッコミどころの除去)、読み手の負荷の管理、視点と語り、演出の抑制、LLM っぽい空句の禁止、冗長の排除を定める。日本語で技術書の章、草稿、記事、解説文を書くとき、または推敲・リライトするときに使用する。

日本語技術文書の文章規範

日本語で技術的な原稿(書籍の章、記事、解説文)を書く・推敲するときは、以下の規範に従う。

整形

@scripting
scripting / demo.js
Created June 12, 2026 14:40
A script used to generate Wikipedia-style pages via ChatGPT
//mypedia -- build a short, balanced reference page about a name, using ChatGPT.
const name = args [0];
if (name === undefined) {
console.log ("Usage: belt mypedia.belt \"name\"");
}
else {
//first-cut prompt -- refine the wording here.
const prompt = "Build a short, balanced reference page about " + name + ". "
+ "Follow the structure of a Wikipedia article but keep it brief -- an outline of the basic facts, not a long detailed treatment. "
name code-refactor-review
description Reviews code changes for reuse, composition, codebase consistency, and slop. Use when asked to review PRs/diffs, check code reuse, composition, cleanliness, or whether code fits the codebase.

Code Refactor Review

Review code changes the way Sahaj usually asks for review: go deep on reuse, composition, codebase consistency, and anything that reads like slop.

First Pass

Method-K: AGI Architecture (Minecraft-based Adaptive Intelligence)

Author and Sole Rightsholder: AGI885
Priority Registration Date: June 2026
Status: All Rights Reserved


🗺️ System Overview

This repository formally establishes the intellectual property, copyright, and architectural priority of the user AGI885 (GitHub: AGI885) for the development of a True Artificial General Intelligence (AGI) system. The framework is built upon the principles of evolutionary adaptation and causal learning within isolated 2D and 3D simulation environments (including, but not limited to, the Minecraft platform). The core architecture completely bypasses the standard next-token prediction paradigm of modern LLMs, transitioning instead to genuine causal learning and establishing an emergent sense of agency.

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

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